@inproceedings{alipoormolabashi-schulte-im-walde-2020-variants,
title = "Variants of Vector Space Reductions for Predicting the Compositionality of {E}nglish Noun Compounds",
author = "Alipoormolabashi, Pegah and
Schulte im Walde, Sabine",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan and
Tsai, Alicia and
Anastasopoulos, Antonios and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.13",
doi = "10.18653/v1/2020.winlp-1.13",
pages = "51--54",
abstract = "Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task.",
}
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%0 Conference Proceedings
%T Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds
%A Alipoormolabashi, Pegah
%A Schulte im Walde, Sabine
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F alipoormolabashi-schulte-im-walde-2020-variants
%X Predicting the degree of compositionality of noun compounds is a crucial ingredient for lexicography and NLP applications, to know whether the compound should be treated as a whole, or through its constituents. Computational approaches for an automatic prediction typically represent compounds and their constituents within a vector space to have a numeric relatedness measure for the words. This paper provides a systematic evaluation of using different vector-space reduction variants for the prediction. We demonstrate that Word2vec and nouns-only dimensionality reductions are the most successful and stable vector space reduction variants for our task.
%R 10.18653/v1/2020.winlp-1.13
%U https://aclanthology.org/2020.winlp-1.13
%U https://doi.org/10.18653/v1/2020.winlp-1.13
%P 51-54
Markdown (Informal)
[Variants of Vector Space Reductions for Predicting the Compositionality of English Noun Compounds](https://aclanthology.org/2020.winlp-1.13) (Alipoormolabashi & Schulte im Walde, WiNLP 2020)
ACL